A Regularized Raking Estimator for Small-Area Mapping from Forest Inventory Surveys

In this paper, we propose a new estimator for creating expansion factors for survey plots in the US Forest Service (USFS) Forest Inventory and Analysis program. This estimator was previously used in the GIS literature, where it was called Penalized Maximum Entropy Dasymetric Modeling. We show here that the method is a regularized version of the raking estimator widely used in sample surveys. The regularized raking method differs from other predictive modeling methods for integrating survey and ancillary data, in that it produces a single set of expansion factors that can have a general purpose which can be used to produce small-area estimates and wall-to-wall maps of any plot characteristic. This method also differs from other more widely used survey techniques, such as GREG estimation, in that it is guaranteed to produce positive expansion factors. Here, we extend the previous method to include cross-validation, and provide a comparison to expansion factors between the regularized raking and ridge GREG survey calibration.

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A Regularized Raking Estimator for Small Area Mapping from Forest Inventory Surveys

10.20944/preprints201910.0093.v1 ◽

2019 ◽

Author(s):

Nicholas N. Nagle ◽

Todd A. Schroeder ◽

Brooke Rose

Keyword(s):

Forest Inventory ◽

Small Area ◽

Cross Validation ◽

General Purpose ◽

Previous Method ◽

Ancillary Data ◽

Forest Inventory And Analysis ◽

Survey Techniques ◽

Modeling Methods ◽

Single Set

We propose a new estimator for creating expansion factors for survey plots in the USDA Forest Inventory and Analysis program. This is a regularized version of the raking estimator widely used in sample surveys. The regularized raking method differs from other predictive modeling methods for integrating survey and ancillary data in that it produces a single set of expansion factors that can have general purpose use to produce small area estimates and wall-to-wall maps of any plot characteristic. This method also differs from other more widely used survey techniques, such of GREG estimation, in that it is guaranteed to produce positive expansion factors. We extend the previous method here to include cross-validation, and provide a comparison to expansion factors between the regularized raking and ridge GREG survey calibration.

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Estimating Ownerships and Parcels of Nonindustrial Private Forestland in Massachusetts

Northern Journal of Applied Forestry ◽

10.1093/njaf/25.2.93 ◽

2008 ◽

Vol 25 (2) ◽

pp. 93-98 ◽

Cited By ~ 13

Author(s):

David B. Kittredge ◽

Anthony W. D'Amato ◽

Paul Catanzaro ◽

Jennifer Fish ◽

Brett Butler

Keyword(s):

Land Cover ◽

Forest Inventory ◽

Property Tax ◽

Forest Service ◽

The State ◽

Forest Inventory And Analysis ◽

The Us ◽

Us Forest Service ◽

Suburban Development ◽

Private Forestland

Abstract Woodland ownership for three regions of Massachusetts is estimated using property tax assessor data. These data are nonspatially explicit and are based on commercial, industrial, residential, or other activity rather than actual land cover. A heuristic was used to aggregate similar parcels to provide an estimate of actual landownership. The estimated average statewide ownership is 17.9 ac, and when properties less than 10 ac are excluded, the average rises to 42.5 ac. The median ownership varies from east to west in the state across the spectrum of suburban development radiating from the metropolitan Boston area, with the median being 4.8, 7.8, and 8.6 ac in the eastern, central, and western part of the state, respectively. These results are compared with ownership estimates generated by the US Forest Service Forest Inventory and Analysis.

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Separating Duff and Litter for Improved Mass and Carbon Estimates

Southern Journal of Applied Forestry ◽

10.1093/sjaf/33.1.29 ◽

2009 ◽

Vol 33 (1) ◽

pp. 29-34 ◽

Cited By ~ 6

Author(s):

David Chojnacky ◽

Michael Amacher ◽

Michael Gavazzi

Keyword(s):

North Carolina ◽

Organic Matter ◽

Forest Inventory ◽

Mass Ratio ◽

Forest Inventory And Analysis ◽

The Us ◽

Calculated Mass ◽

Density Values ◽

Carbon Load ◽

Forest Soil Organic Matter

Abstract Mass and carbon load estimates, such as those from forest soil organic matter (duff and litter), inform forestry decisions. The US Forest Inventory and Analysis (FIA) Program systematically collects data nationwide: a down woody material protocol specifies discrete duff and litter depth measurements, and a soils protocol specifies mass and carbon of duff and litter combined. Sampling duff and litter separately via the soils protocol would increase accuracy of subsequent bulk density calculations and mass and carbon estimates that use them. At 57 locations in North Carolina, Virginia, and West Virginia, we measured depth, mass, and carbon of duff and litter separately. Duff depth divided by total depth varied from 20% to 56%, duff was 1–4 times denser than litter, and the calculated median carbon-to-mass ratio for hardwood duff (0.37) was less than that for litter (0.45). Using FIA depth measurements, we calculated mass from (1) our mean density values, (2) a mass versus depth regression model we developed, and (3) published density values. Model mass calculations were lower than those using our mean densities, possibly because the latter ignore density differences with layer thickness. Our model could provide valuable mass and carbon estimates if fully developed with future FIA data (duff and litter separated).

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Needs for Small Area Estimation: Perspectives From the US Private Forest Sector

Frontiers in Forests and Global Change ◽

10.3389/ffgc.2021.746439 ◽

2021 ◽

Vol 4 ◽

Author(s):

Steve Prisley ◽

Jeff Bradley ◽

Mike Clutter ◽

Suzy Friedman ◽

Dick Kempka ◽

...

Keyword(s):

Forest Inventory ◽

Small Area ◽

Small Area Estimation ◽

Forest Products ◽

National Forest Inventory ◽

National Forest ◽

Forest Sector ◽

Area Estimation ◽

The Us ◽

Resource Data

The commercial forest sector in the US includes forest landowners and forest products manufacturers, as well as numerous service providers along the supply chain. Landowners (and contractors working for them) manage forestland in part for roundwood production, and manufacturers purchase roundwood as raw material for forest products including building products, paper products, wood pellets, and others. Both types of organizations need forest resource data for applications such as strategic planning, support for certification of sustainable forestry, analysis of timber supply, and assessment of forest carbon, biodiversity, or other ecosystem services. The geographic areas of interest vary widely but typically focus upon ownership blocks or manufacturing facilities and are frequently small enough that estimates from national forest inventory data have insufficient precision. Small area estimation (SAE) has proven potential to combine field data from the national forest inventory with abundant sources of remotely sensed or other resource data to provide needed information with improved precision. Successful implementation of SAE by this sector will require cooperation and collaboration among federal and state government agencies and academic institutions and will require increased funding to improve data collection, data accessibility, and further develop and implement the needed technologies.

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Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service

Canadian Journal of Forest Research ◽

10.1139/x05-222 ◽

2005 ◽

Vol 35 (12) ◽

pp. 2968-2980 ◽

Cited By ~ 19

Author(s):

Ronald E McRoberts ◽

Geoffrey R Holden ◽

Mark D Nelson ◽

Greg C Liknes ◽

Dale D Gormanson

Keyword(s):

Satellite Imagery ◽

Forest Inventory ◽

Forest Service ◽

Analysis Program ◽

Ancillary Data ◽

Usda Forest Service ◽

Forest Inventory And Analysis ◽

New Approach ◽

Areas Of Interest ◽

Effective Source

Forest inventory programs report estimates of forest variables for areas of interest ranging in size from municipalities, to counties, to states or provinces. Because of numerous factors, sample sizes are often insufficient to estimate attributes as precisely as is desired, unless the estimation process is enhanced using ancillary data. Classified satellite imagery has been shown to be an effective source of ancillary data that, when used with stratified estimation techniques, contributes to increased precision with little corresponding increase in cost. Stratification investigations conducted by the Forest Inventory and Analysis program of the USDA Forest Service are reviewed, and a new approach to stratification using satellite imagery is proposed. The results indicate that precision may be substantially increased for estimates of both forest area and volume per unit area.

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TreeMap, a tree-level model of conterminous US forests circa 2014 produced by imputation of FIA plot data

Scientific Data ◽

10.1038/s41597-020-00782-x ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Karin L. Riley ◽

Isaac C. Grenfell ◽

Mark A. Finney ◽

Jason M. Wiener

Keyword(s):

Forest Type ◽

Fire Effects ◽

Tree Level ◽

Forest Plot ◽

National Scale ◽

Forest Inventory And Analysis ◽

The Us ◽

Us Forest Service ◽

Level Model ◽

Imputation Approach

AbstractA 30 × 30m-resolution gridded dataset of forest plot identifiers was developed for the conterminous United States (CONUS) using a random forests machine-learning imputation approach. Forest plots from the US Forest Service Forest Inventory and Analysis program (FIA) were imputed to gridded c2014 landscape data provided by the LANDFIRE project using topographic, biophysical, and disturbance variables. The output consisted of a raster map of plot identifiers. From the plot identifiers, users of the dataset can link to a number of tree- and plot-level attributes stored in the accompanying tables and in the publicly available FIA DataMart, and then produce maps of any of these attributes, including number of trees per acre, tree species, and forest type. Of 67,141 FIA plots available, 62,758 of these (93.5%) were utilized at least once in imputation to 2,841,601,981 forested pixels in CONUS. Continuous high-resolution forest structure data at a national scale will be invaluable for analyzing carbon dynamics, habitat distributions, and fire effects.

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Measuring sustainability using the US Forest Inventory and Analysis Program.

Sustainable forestry: from monitoring and modelling to knowledge management and policy science ◽

10.1079/9781845931742.0199 ◽

2009 ◽

pp. 199-207

Author(s):

C. T. Scott ◽

W. H. McWilliams

Keyword(s):

Forest Inventory ◽

Analysis Program ◽

Forest Inventory And Analysis ◽

The Us

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A primer for nonresponse in the US forest inventory and analysis program

Environmental Monitoring and Assessment ◽

10.1007/s10661-011-2051-5 ◽

2011 ◽

Vol 184 (3) ◽

pp. 1423-1433 ◽

Cited By ~ 8

Author(s):

Paul L. Patterson ◽

John W. Coulston ◽

Francis A. Roesch ◽

James A. Westfall ◽

Andrew D. Hill

Keyword(s):

Forest Inventory ◽

Analysis Program ◽

Forest Inventory And Analysis ◽

The Us

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A Unique Academic–Government Collaboration Yields First Report of Detailed Habitat Description for Haemaphysalis longicornis (Ixodida: Ixodidae) in Madison County, KY

Journal of Medical Entomology ◽

10.1093/jme/tjab061 ◽

2021 ◽

Author(s):

J T Vogt ◽

B D Allen ◽

D Paulsen ◽

R T Trout Fryxell

Keyword(s):

United States ◽

Forest Inventory ◽

Forest Service ◽

Haemaphysalis Longicornis ◽

Analysis Program ◽

Ancillary Data ◽

Usda Forest Service ◽

Forest Inventory And Analysis ◽

First Report ◽

Madison County

Abstract Haemaphysalis longicornis Neumann, Asian longhorned tick, was collected in Madison County, Kentucky, United States as part of an ongoing collaborative-tick surveillance project. This is the first collection of this invasive tick that includes ancillary data on habitat and landscape features derived from the USDA Forest Service, Forest Inventory and Analysis program.

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